Photocatalytic Removal of Metronidazole Antibiotics from Water Using Novel Ag-N-SnO2 Nanohybrid Material.

In this study, we employed a straightforward synthetic approach using the sol-gel method to fabricate a novel photocatalyst, Ag and N co-doped SnO2 (Ag-N-SnO2). The synthesized photocatalysts underwent characterization through various techniques including XRD, FTIR, FESEM-EDS, TEM, UV-vis DRS, BET, and XPS. The UV-vis DRS results confirmed a reduction in the bandgap energy of Ag-N-SnO2, leading to enhanced absorption of visible light. Additionally, TEM data demonstrated a smaller particle size for Ag-N-SnO2, and BET analysis revealed a significant increase in surface area compared to SnO2.The efficiency of the Ag-N-SnO2 photocatalyst in degrading metronidazole (MNZ) under natural sunlight surpassed that of SnO2. Under optimal conditions (Ag-N-SnO2 concentration of 0.4 g/L, MNZ concentration of 10 mg/L, pH 9, and 120 min of operation), the highest MNZ photocatalytic removal reached 97.03%. The reaction kinetics followed pseudo-first-order kinetics with a rate constant of 0.026 min-1. Investigation into the mineralization of MNZ indicated a substantial decrease in total organic carbon (TOC) values, reaching around 56% in 3 h of sunlight exposure. To elucidate the photocatalytic degradation mechanism of MNZ with Ag-N-SnO2, a scavenger test was employed which revealed the dominant role of •O2-. The results demonstrated the reusability of Ag-N-SnO2 for up to four cycles, highlighting its cost-effectiveness and environmental friendliness as a photocatalyst.

Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies.

The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape. Our study addresses four methodically-tailored and patient-centric Research Questions, primarily examining 138 relevant scientific papers. This scoping review also explores how the pervasiveness of blockchain technology can help to empower patients in terms of access, awareness, and control. Finally, this scoping review leverages the insights gleaned from this study and contributes to the body of knowledge by proposing a patient-centric blockchain-based framework. This work will envision orchestrating three essential elements with harmony: scientific advancement (Healthcare and EHR), technology integration (Blockchain Technology), and patient empowerment (access, awareness, and control).

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

BACKGROUND: Diabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring. OBJECTIVE: Our study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy. METHODS: Our system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy. RESULTS: When using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement. CONCLUSIONS: Our pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management.

The COVID-19 pandemic has caused a global crisis with 47,209,305 confirmed cases and 1,209,505 confirmed deaths worldwide as of November 2, 2020. Forecasting confirmed cases and understanding the virus dynamics is necessary to provide valuable insights into the growth of the outbreak and facilitate policy-making regarding virus containment and utilization of medical resources. In this study, we applied a mathematical epidemic model (MEM), statistical model, and recurrent neural network (RNN) variants to forecast the cumulative confirmed cases. We proposed a reproducible framework for RNN variants that addressed the stochastic nature of RNN variants leveraging z-score outlier detection. We incorporated heterogeneity in susceptibility into the MEM considering lockdowns and the dynamic dependency of the transmission and identification rates which were estimated using Poisson likelihood fitting. While the experimental results demonstrated the superiority of RNN variants in forecasting accuracy, the MEM presented comprehensive insights into the virus spread and potential control strategies.

Health Internet Technology for Chronic Conditions: Review of Diabetes Management Apps.

BACKGROUND: Mobile health (mHealth) smartphone apps have shown promise in the self-management of chronic disease. In today's oversaturated health app market, selection criteria that consumers are employing to choose mHealth apps for disease self-management are of paramount importance. App quality is critical in monitoring disease controls but is often linked to consumer popularity rather than clinical recommendations of effectiveness in disease management. Management of key disease variances can be performed through these apps to increase patient engagement in disease self-management. This paper provides a comprehensive review of features found in mHealth apps frequently used in the self- management of diabetes. OBJECTIVE: The purpose of this study was to review features of frequently used and high consumer-rated mHealth apps used in the self-management of diabetes. This study aimed to highlight key features of consumer-favored mHealth apps used in the self-management of diabetes. METHODS: A 2-fold approach was adopted involving the Apple iOS store and the Google search engine. The primary search was conducted on the Apple iOS store using the term "diabetes apps" (device used: Apple iPad). The top 5 most frequently used mHealth apps were identified and rated by the number of consumer reviews, app ratings, and the presence of key diabetes management features, such as dietary blood glucose, A1C, insulin, physical activity, and prescription medication. A subsequent Google search was conducted using the search term "best Apple diabetes apps." The top 3 search results-"Healthline," "Everyday Health," and "Diabetes Apps-American Diabetes Association"-were explored. RESULTS: In total, 12 mHealth apps were reviewed due to their appearing across 4 evaluated sources. Only 1 health app-Glucose Buddy Diabetes Tracker-appeared as the most frequently used within the Apple iOS store and across the other 3 sources. The OneTouch Reveal app ranked first on the list in the iOS store with 39,000 consumer reviews and a rating of 4.7 out of 5.0 stars but only appeared in 1 of the other 3 sources. Blood glucose tracking was present across all apps, but other disease management features varied in type with at least 3 of the 5 key features being present across the 12 reviewed apps. Subscription cost and integration needs were present in the apps which could impact consumers' decision to select apps. Although mobile app preference was assessed and defined by the number of consumer reviews and star ratings, there were no scientific standards used in the selection and ranking of the health apps within this study. CONCLUSIONS: mHealth apps have shown promise in chronic disease management, but a surge in development of these nonregulated health solutions points to a need for regulation, standardization, and quality control. A governing body of health IT professionals, clinicians, policymakers, payors, and patients could be beneficial in defining health app standards for effective chronic disease management. Variabilities in features, cost, and other aspects of management could be reduced by regulatory uniformity, which would increase patient engagement and improve disease outcomes.

Wi-COVID: A COVID-19 symptom detection and patient monitoring framework using WiFi.

The current SARS-CoV-2, better know as COVID-19, has emerged as a serious pandemic with life-threatening clinical manifestations and a high mortality rate. One of the major complications of this disease is the rapid and dangerous pulmonary deterioration that can lead to critical pneumonia conditions, resulting in death. The current healthcare system around the world faces the potential problem of lacking resources to assist a large number of patients at the same time; then, the non-critical patients are mostly referred to perform self-isolation/quarantine at home. This pandemic has placed new demands on the health systems world, asking for novel, rapid and secure ways to monitor patients in order to detect and quickly report patient's symptoms to the healthcare provider, even if they are not in the hospital. While tremendous efforts have been done to develop technologies to detect the virus, create the vaccine, and stop the spread of the disease, it is also important to develop IoT technologies that can help track and monitor diagnosed COVID-19 patients from their homes. In this paper, we explore the possibility of monitoring respiration rates (RR) of COVID-19 patients using a widely-available technology at home - WiFi. Using the at-home WiFi signals, we propose Wi-COVID, a non-invasive and non-wearable technology to monitor the patient and track RR for the healthcare provider. We first introduce the currently available applications that can be done using WiFi signals. Then, we propose the framework scheme for an end-to-end non-invasive monitoring platform of the COVID-19 patients using WiFi. Finally, we present some preliminary results of the proposed framework. We envision the proposed platform as a life-changing technology that leverages WiFi technology as a non-wearable and non-invasive way to monitor COVID-19 patients at home.

Features of Sizing and Enumeration of Silica and Polystyrene Nanoparticles by Nanoparticle Tracking Analysis (NTA).

Nanoparticle Tracking Analysis (NTA) allows for the simultaneous determination of both size and concentration of nanoparticles in a sample. This study investigates the accuracy of particle size and concentration measurements performed on an LM10 device. For experiments, standard nanoparticles of different sizes composed of two materials with different refractive indices were used. Particle size measurements were found to have a decent degree of accuracy. This fact was verified by the manufacturer-reported particle size-determined by transmission electron microscopy (TEM)-as well as by performed scanning electron microscopy (SEM) measurements. On the other hand, concentration measurements resulted in overestimation of the particle concentration in majority of cases. Thus, our findings confirmed the accuracy of nanoparticle sizing performed by the LM10 instrument and highlighted the overestimation of particle concentration made by this device. In addition, an approach of swift correction of the results of concentration measurements received for samples is suggested in the presented study.

Epileptic Seizure Detection for Imbalanced Datasets Using an Integrated Machine Learning Approach.

Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures. Machine Learning algorithms have been used to classify epilepsy from Electroencephalograms (EEG) data. These algorithms exhibited reduced performance when classes are imbalanced. This work presents an integrated machine learning approach for epilepsy detection, which can effectively learn from imbalanced data. This approach utilizes Principal Component Analysis (PCA) at the first stage to extract both high- and low- variant Principal Components (PCs), which are empirically customized for imbalanced data classification. Conventionally, PCA is used for dimension reduction of a dataset leveraging PCs with high variances. In this paper, we propose a model to show that PCs associated with low variances can capture the implicit pattern of minor class of a dataset. The selected PCs are then fed into different machine learning classifiers to predict seizures. We performed experiments on the Epileptic Seizure Recognition dataset to evaluate our model. The experimental results show the robustness and effectiveness of the proposed model.

OpenPharma Blockchain on FHIR: An Interoperable Solution for Read-Only Health Records Exchange through Blockchain and Biometrics.

The healthcare system in the United States is unique. From payor to provider, patients have the freedom of choice. This creates a complicated and profitable paradigm of care. Legislation defines government expectations of data exchange; however, the methods are left to the discretion of the stakeholders. Today, devices and programs are not built to unified standards, thus they do not share data easily. This communication between software is known as interoperability. We address the health data interoperability by leveraging Fast Health Interoperable Resource (FHIR) standard, a viewer of FHIR called OpenPharma, and Blockchain technology. Our proof of concept, called "OpenPharma Blockchain on FHIR" (OBF), is interoperable by design and grants clinicians access to patient records using a combination of data standards, distributed applications, patient-driven identity management, and the Ethereum blockchain. OBF is a trustless, secure, decentralized, and vendor-independent method for information exchange. It is easy to implement and places the control of records with the patients.